Thank you to Google and the Magenta team for making such an excellent neural network synthesizer! This version of GANSynth has been altered to accept stereo files and a single dataset folder as an input pipeline. Self-attention has been implemented whenever the generator or discriminator makes a spectrogram with an area of 8192 pixels. This is consistent with the original “Self-Attention Generative Adversarial Networks” paper (Zhang et al., 2018.)
You can use any sample rate as long as all your files are the same length and you don’t exceed 67072 total samples, which is the ceiling for the 128 X 1024 spectrogram implemented in the architecture. 1.5 second samples at 44100 kHz is a good choice—16-bit audio only. Also, be sure to end all your input files with exactly “.wav”, and don't use any unusual characters or spaces in the file names.
The algorithm has been tested on Colaboratory/Mac, so you may have to do a bit of file-system tweaking for other configurations. And it only works on TensorFlow version 1.15 and below.
This is the training script:
%tensorflow_version 1.15
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
!pip install bezier
!python gansynth_train.py --hparams='{"train_data_path":"path", "train_root_dir":"path", "channel_mode":"stereo", "audio_length":1.5, "sample_rate":44100}'Note the “!pip install bezier” line. That’s needed for the attack and release envelope curves. The bottommost line is the most important, particularly the “—hparams” section. This is where you tell the algorithm how to train. “train_data_path” is the location of your input audio, and “train_root_dir” is where you want your training files to land. “channel_mode” lets you choose either a mono or a stereo model. The program will convert input files to whatever mode you choose here. “audio_length” is the length of your dataset files, in seconds. “sample_rate” is the audio sample rate you are using.
There are many hyperparameters originally coded by Google as well, including learning rate and the number of epochs (referred to as “number of images” in the code.) You can alter either of these from the command line or in the "model.py" file.
Once you’ve trained a model, it’s time to generate. GANSynth uses MIDI files for this purpose, and it can be finicky about what MIDI it likes. If you’re getting errors, try increasing the note length. The generation script looks something like this:
%tensorflow_version 1.15
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
!pip install bezier
!python gansynth_generate.py --ckpt_dir=path --output_dir=path --midi_file=path --attack_percent=1 --attack_slope=0.5 --release_percent=1 --release_slope=0.5You can see that the bottom line is different from the training script. There are no “—hparams” this time, but the idea is the same—you’re telling the algorithm how you want it to generate audio. "--ckpt_dir" should be the same as "train_data_path" from the training script. "--output_dir" is where you want your generated audio file to go, and "--midi_file" is where your MIDI file lives. "--attack_slope" and "--release_slope" pertain to the bezier curves—the range is 0 to 1, and higher numbers mean quicker changes. "--attack_percent" and "--release_percent" (range: 0-100) control fades over the entire length of the note, which means that values above 50 may result in overlapping fades.